Relational Reinforcement Learning

该项目为关系强化学习开发新的算法，程序和理论。强化学习（RL）旨在设计和构建智能程序，通过采取行动并从环境中接收强化或反馈来优化其行为。目前的研究主要集中在强化学习的问题域中的环境状态表示为命题的向量。许多真实的领域，如物流规划，信息提取，语义信息检索从Web需要关系表示，不服从命题学习方法。该项目通过扩展到关系设置技术，如分层学习，构建和利用世界模型，学习价值函数和策略的紧凑表示，以及将规划与学习相结合，开发解决这些领域中强化学习问题的方法。该项目更广泛的影响包括发现更通用和有效的强化学习算法，开发新的物流规划解决方案，以及更有效的与Web交互以检索有用信息的方法。

This project develops new algorithms, programs, and theory for Relational Reinforcement Learning. Reinforcement Learning (RL) seeks to design and build intelligent programs that optimize their behavior by taking actions and receiving reinforcement or feedback from the environment. Current research in RL is focused mainly on problem domains where the environmental states are represented as vectors of propositions. Many real world domains such as logistics planning, information extraction, and semantic information retrieval from the Web require relational representations and are not amenable to propositional learning methods. This project develops methods to solve reinforcement learning problems in such domains by extending to the relational setting techniques such as hierarchical learning, building and exploiting models of the world, learning compact representations of value functions and policies, and combining planning with learning. The broader impacts of the project include discovery of more general and effective reinforcement learning algorithms, development of new heuristics to solve logistics planning problems, and more effective ways to interact with the Web to retrieve useful information.